通过知识编译实现反事实查询的可易处理边界计算
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
AI总结：
针对Pearl结构因果模型中反事实等部分可识别查询的边界计算问题，提出基于单次符号知识编译为带符号参数算术电路的方法，结合并行化技术，实现最高达一个数量级的推理加速。
摘要：
我们讨论了Pearl结构因果模型中部分可识别查询（如反事实查询）的边界计算问题。近期提出的迭代EM方案通过对初始化参数采样，得到这些边界的内近似值。该方法需要对多个模型执行（贝叶斯网络）查询，这些模型共享相同的结构方程和拓扑结构，但外生概率不同。这种设置使得将底层模型编译为算术电路具有优势，从而大幅提升推理速度。我们展示了如何通过单次符号知识编译得到带有符号参数的电路结构，在计算不同查询时将符号参数替换为实际值。我们还讨论了进一步加速边界计算的并行化技术。与标准贝叶斯网络推理的对比实验显示，该方法具有显著的计算优势，加速比最高可达一个数量级。
英文摘要：
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters. Such a method requires multiple (Bayesian network) queries over models sharing the same structural equations and topology, but different exogenous probabilities. This setup makes a compilation of the underlying model to an arithmetic circuit advantageous, thus inducing a sizeable inferential speed-up. We show how a single symbolic knowledge compilation allows us to obtain the circuit structure with symbolic parameters to be replaced by their actual values when computing the different queries. We also discuss parallelisation techniques to further speed up the bound computation. Experiments against standard Bayesian network inference show clear computational advantages with up to an order of magnitude of speed-up.
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